At a recent live recording of the RIoT Underground podcast, host Tom Snyder sat down with Ryan Abel, Managing Director of Atomic Object’s Raleigh office, to explore the ever-evolving world of artificial intelligence, its practical implications, and the importance of a strong, intentional company culture on the successful adoption of these technologies for companies.

The event marked RIoT’s second live podcast session, drawing a diverse audience eager to gain insights from leaders at the forefront of tech innovation.

RIoT’s Mission and Momentum

Tom opened the evening by sharing RIoT’s journey as a nonprofit economic development organization focused on technology. Celebrating nearly 10 years of impact, RIoT continues to champion entrepreneurship through startup accelerators across North Carolina and Virginia, while keeping community events like this podcast recording free and accessible, thanks to generous public and private sponsorship.

Meet the Guest: Ryan Abel of Atomic Object

Ryan Abel is Managing Partner at Atomic Object, where he blends deep technical expertise with a passion for empowering teams and clients alike. A lifelong digital native, Ryan discovered early on that technology wasn’t just a hobby—it could be a career. Ryan introduced Atomic Object as a custom software design and development firm founded in 2001 with a strong emphasis on sustainable growth and culture. Privately held and employee-owned, Atomic’s mission centers around being the go-to local software consultancy. A signature question for all RIoT Underground podcast guests is: “If a movie or TV show were made about your life, who would you want to play you, and why?” Ryan answered with a laugh, choosing Andy Samberg as his ideal on-screen double—citing his own easygoing nature, creative mindset, and appreciation for off-the-cuff humor as reasons for the fitting match.

Navigating the AI Hype: Where Are We Really?

Both Tom and Ryan acknowledged that while the AI buzz is loud, the actual implementation in business environments is still in its infancy. Ryan categorized today’s AI tools as operational enhancers—boosting productivity and efficiency in areas like meeting transcription, data handling, and software prototyping.

When asked whether AI would remain an efficiency tool or evolve into a true value creator, Ryan predicted a gradual evolution. He drew comparisons to past tech transformations like the cloud shift, which initially threatened to eliminate jobs but ultimately created new roles. AI, he suggested, could elevate job roles by automating mundane tasks and enabling humans to focus on more meaningful work.

Technology with Integrity: How Culture Shapes AI Implementation

While Ryan and Tom explored advanced AI concepts like Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP), the conversation consistently circled back to a deeper truth: technology alone doesn’t drive innovation—culture does.

Ryan challenged the idea that organizational culture is merely a “soft” concept. Instead, he framed it as a strategic advantage—particularly in an era where AI is transforming how work is done. Companies that cultivate open communication, support lifelong learning, and emphasize emotional intelligence are the ones best positioned to adapt and thrive.

Technologies like RAG and MCP offer powerful ways to streamline workflows. RAG allows organizations to use their internal knowledge more effectively, tailoring AI responses to be highly relevant. MCP, meanwhile, abstracts away backend complexity, making it easier for systems to interact. But these tools are only as powerful as the cultures that support their adoption.

“RAG is the secret that gives organizations a bit of a moat,” Ryan explained. “And if you publish things in a way MCP can understand, it just knows how to get what it needs.”

Yet without a culture of collaboration, experimentation, and mutual support, even the most advanced systems are likely to be underutilized or misunderstood.

Teach and Learn: The Cultural Backbone

Ryan spotlighted “Teach and Learn” as a core cultural value. This principle transforms every team member into both a learner and a mentor. Rather than top-down AI mandates, Ryan champions bottom-up adoption—a model where employees organically explore tools, share discoveries, and normalize failure as a learning tool.

“People tend to listen to their peers more than management,” Ryan said. “If you can get people excited about it, that’s how it becomes most effective.”

This culture of peer-to-peer learning builds trust and accelerates AI integration across teams. Transparency about what works—and what doesn’t—leads to more authentic experimentation and less fear of failure.

Bottom-Up Innovation and the Ethics of Openness

The conversation turned to concerns about closed ecosystems, as companies like Slack and Cloudflare are starting to restrict API access and charge for AI data usage—a trend that threatens to silo innovation.

“It feels like we’re forming a new 900-pound gorilla overnight with OpenAI,” expressing concern that AI could become another proprietary battleground, limiting innovation and interoperability.

Ryan cautioned against the industry’s drift toward closed ecosystems, noting the rising dominance of players like OpenAI. These shifts aren’t just technical; they reflect deeper values. A company that embraces transparency and openness in its data practices is often one that prioritizes ethics, inclusion, and long-term thinking over short-term gains.

“Technical decisions are inherently cultural ones,” Ryan noted. Choices around data sharing, vendor lock-in, or fast-track AI implementation all echo a company’s foundational values.

Responsible AI Starts with Cultural Maturity

As the conversation turned to security and responsible use, Ryan made a compelling case: organizations cannot afford to treat AI like a black box. Without internal transparency, critical thinking, and psychological safety, teams will struggle to responsibly manage what AI is doing—and why.

It’s not enough to have governance policies on paper. Teams must feel empowered to ask tough questions, flag ethical risks, and slow down when something doesn’t seem right. This level of vigilance can’t be mandated—it must be nurtured through culture.

Hiring for Adaptability, Not Just Ability

All of this culminated as Ryan predicted that AI will redefine what matters in hiring. As hard skills become easier to replicate with automation, human traits—adaptability, curiosity, empathy—will rise in importance. Organizations stuck in traditional hiring models focused solely on technical prowess risk missing out on talent that can truly thrive in an AI-driven future.

“Hire for adaptability,” Ryan advised. “Skills can be taught. Values are much harder to train.”

The Takeaway

From cutting-edge tools like RAG and MCP to big-picture questions about data ethics, hiring, and responsible innovation, Ryan and Tom covered a lot of ground. But beneath all the technical details lies a deeper, more human question: is organizational culture vitale to determining whether AI creates value or chaos? Do organizations need to prioritize continuous learning, transparency, and trust to be successful and stand out in this new AI world?

Tune in to the full episode to explore how culture, leadership, and mindset might just be the real infrastructure behind successful AI implementation—and decide for yourself what matters most.